This paper presents a technique to estimate precipitation over Indian land (6–36°N, 65–99°E) at spatial grid using tropical rainfall measuring mission (TRMM) microwave imager (TMI) observations. It adopts the methodology recently developed by Mishra (2012) to monitor the rainfall over the land portion. Regional scattering index (SI) developed for Indian region and polarization corrected temperature (PCT) have been utilized in this study. These proxy rain variables (i.e., PCT and SI) are matched with rainfall from precipitation radar (PR) to relate rain rate with PCT, SI, and their combination. Retrieval techniques have been developed using nonlinear relationship between rain and proxy variables. The results have been compared with the observations (independent of training data set) from PR. Results have also been validated with the observations from automatic weather station (AWS) rain gauges. It is observed from the validation results that nonlinear algorithm using single variable SI underestimates the low rainfall rates (below 20？mm/h) but overestimates the high rain rates (above 20？mm/h). On the other hand, algorithm using PCT overestimates the high rain rates (above 25？mm/h). Validation results with rain gauges show a CC of 0.68 and RMSE of 4.76？mm when both SI and PCT are used. 1. Introduction High rainfall events in India have increased by 50% during past 50 years [1]. Hence an accurate prediction of high rainfall event is essential for prevention and management of disasters. In India, there is a low spatial density of automatic rain gauges and Doppler Weather Radars (DWRs). So satellite-based rainfall estimates are highly valuable for synoptic situations. Rainfall estimations based on infrared (IR) measurements from satellite have large errors because IR radiances from cloud tops have only indirect and weak relationship with surface rainfall [2, 3]. On the other hand, satellite microwave measurements provide more direct estimates of rainfall. Microwave algorithms are generally classified as statistical or physical [4]. Statistical algorithms (e.g., [5]) use observed data to derive an empirical relationship between brightness temperature and precipitation. Physical algorithm (e.g., [6–9]) uses a data base of radiative transfer calculations based on atmospheric profiles, which are compared with an observed set of brightness temperature. The higher the number of channels used, the greater the chances of finding an accurate hydrometeor profile in data base. Precipitation estimation algorithms from microwaves using radiative transfer models have been
References
[1]
B. N. Goswami, V. Venugopal, D. Sangupta, M. S. Madhusoodanan, and P. K. Xavier, “Increasing trend of extreme rain events over India in a warming environment,” Science, vol. 314, no. 5804, pp. 1442–1445, 2006.
[2]
A. Mishra, R. M. Gairola, A. K. Varma, and V. K. Agarwal, “Remote sensing of precipitation over Indian land and oceanic regions by synergistic use of multi-satellite sensors,” Journal of Geophysical Research, vol. 115, Article ID D08106, 12 pages, 2010.
[3]
A. K. Mishra, R. M. Gairola, A. K. Varma, and V. K. Agarwal, “Improved rainfall estimation over the Indian region using satellite infrared technique,” Advances in Space Research, vol. 48, no. 1, pp. 49–55, 2011.
[4]
A. Mishra, Remote Sensing of Precipitation from Satellite Infrared and Microwave Observations Over Indian Tropics, Lambert Academic Publication, Berlin, Germany, 2012.
[5]
R. F. Adler, G. J. Huffman, and P. R. Keehn, “Global tropical rain estimates from microwave-adjusted geosynchronous IR data,” Remote Sensing Reviews, vol. 11, no. 1–4, pp. 125–152, 1994.
[6]
K. F. Evans, J. Turk, T. Wong, and G. L. Stephens, “A Bayesian approach to microwave precipitation profile retrieval,” Journal of Applied Meteorology, vol. 34, no. 1, pp. 260–278, 1995.
[7]
C. Kummerow, Y. Hong, W. S. Olson et al., “The evolution of the Goddard profiling algorithm (GPROF) for rainfall estimation from passive microwave sensors,” Journal of Applied Meteorology, vol. 40, no. 11, pp. 1801–1820, 2001.
[8]
A. Mugnai and E. A. Smith, “Radiative transfer to space through a precipitating cloud at multiple microwave frequencies. Part1: model description,” Journal of Applied Meteorology,, vol. 27, no. 9, pp. 1055–1073, 1988.
[9]
N. Viltard, C. Burlaud, and C. D. Kummerow, “Rain retrieval from TMI brightness temperature measurements using a TRMM PR-based database,” Journal of Applied Meteorology and Climatology, vol. 45, no. 3, pp. 455–466, 2006.
[10]
R. Wu and J. A. Weinman, “Microwave radiances from precipitating clouds containing aspherical ice, combined phase, and liquid hydrometeors,” Journal of Geophysical Research, vol. 89, no. 5, pp. 7170–7178, 1984.
[11]
C. Kummerow, R. A. Mack, and I. M. Hakkarinen, “A self-consistency approach to improve microwave rainfall rate estimation from space,” Journal of Applied Meteorology, vol. 28, no. 9, pp. 869–884, 1989.
[12]
N. C. Grody, “Classification of snow cover and precipitation using the special sensor Microwave Imager,” Journal of Geophysical Research, vol. 96, no. 4, pp. 7423–7435, 1991.
[13]
R. R. Ferraro and G. F. Marks, “The development of SSM/I rain rate retrieval algorithms using ground based radar measurements,” Journal of Atmospheric and Oceanic Technology, vol. 12, pp. 755–770, 1995.
[14]
W. J. Chen, M. D. Tsai, G. R. Liu, and M. H. Chang, “The study of rainfall derived from TRMM Microwave Imager data over Taiwan land—using Scattering Index method,” Atmospheric Sciences, vol. 33, pp. 277–300, 2005.
[15]
A. Mishra, R. M. Gairola, A. K. Varma, A. Sarkar, and V. K. Agarwal, “Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data,” Advances in Space Research, vol. 44, no. 7, pp. 815–823, 2009.
[16]
N. C. Grody, “Precipitation monitoring over land from satellites by microwave radiometry,” in Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS '84), ESA SP-215, pp. 417–423, Strasbourg, France, August 1984.
[17]
R. W. Spencer, H. M. Goodman, and R. E. Hood, “Precipitation retrieval over land and ocean with the SSM/I: identification and characteristics of the scattering signal,” Journal of Atmospheric and Oceanic Technology, vol. 6, no. 2, pp. 254–273, 1989.
[18]
C. Kidd and E. C. Barret, “The use of passive microwave imagery in rainfall monitoring,” Remote Sensing Reviews, vol. 4, no. 2, pp. 415–450, 1990.
[19]
R. Kumar, R. M. Gairola, A. Mishra, A. K. Varma, and I. M. L. Das, “Evaluation of precipitation features in high-frequency SSM/I measurements over Indian land and oceanic regions,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 3, pp. 373–377, 2009.
[20]
S. Nativi, E. C. Barrett, and M. J. Beaumont, “Microwave monitoring of rainfall: intercomparisons of data from the Chilbolton radar and the DMSP-SSM/I,” Meteorological Applications, vol. 4, no. 2, pp. 101–114, 1997.
[21]
M. D. Tsai, W. J. Chen, and J. L. Wang, “Implementing polarization-corrected temperature into typhoon rainfall estimates over Taiwan from TRMM/TMI data,” Terrestrial, Atmospheric and Oceanic Sciences, vol. 21, no. 4, pp. 697–712, 2010.
[22]
T. S. Biscaro and C. A. Morales, “Continental passive microwave-based rainfall estimation algorithm: application to the Amazon Basin,” Journal of Applied Meteorology and Climatology, vol. 47, no. 7, pp. 1962–1981, 2008.
[23]
R. V. Calheiros and I. Zawadzki, “Reflectivity-rain rate relationships for radar hydrology in Brazil,” Journal of Climate & Applied Meteorology, vol. 26, no. 1, pp. 118–132, 1987.
[24]
G. Liu and J. A. Curry, “An investigation of the relationship between emission and scattering signals in SSM/I data,” Journal of the Atmospheric Sciences, vol. 55, no. 9, pp. 1628–1643, 1998.
[25]
M. C. Todd and J. O. Bailey, “Estimates of rainfall over the United Kingdom and surrounding seas from the SSM/I using the polarization corrected temperature algorithm,” Journal of Applied Meteorology, vol. 34, no. 6, pp. 1254–1265, 1995.
[26]
Z. Yao, W. Li, Y. Zhu, B. Zhao, and Y. Chen, “Remote sensing of precipitation on the Tibetan Plateau using the TRMM Microwave Imager,” Journal of Applied Meteorology, vol. 40, no. 8, pp. 1381–1392, 2001.
[27]
B. Zhao, Z. Yao, W. Li et al., “Rainfall retrieval and flooding monitoring in China using TRMM Microwave Imager (TMI),” Journal of the Meteorological Society of Japan, vol. 79, no. 1, pp. 301–315, 2001.
[28]
C. Mahesh, S. Prakash, V. Sathiyamoorthy, and R. M. Gairola, “Artificial neural network based microwave precipitation estimation using scattering index and polarization corrected temperatures,” Atmospheric Research, vol. 102, no. 3, pp. 358–364, 2011.
[29]
A. Mishra, “Estimation of heavy rainfall during cyclonic storms from microwave observations using nonlinear approach over Indian Ocean,” Natural Hazards, vol. 63, no. 2, pp. 673–683, 2012.
[30]
T. Iguchi, T. Kozu, R. Meneghini, J. Awaka, and K. I. Okamoto, “Rain-profiling algorithm for the TRMM precipitation radar,” Journal of Applied Meteorology, vol. 39, no. 12, pp. 2038–2052, 2000.
